Project summary Our long-term goal is to demonstrate the utility of ultrasound for OA assessment, standardize its acquisition and scoring, and promote increased uptake of US for use in clinical, research, and trial settings. Knee osteoarthritis (KOA) is highly prevalent and frequently debilitating. Development of potential treatments has been hampered by the heterogenous nature of this common chronic condition, which is characterized by a number of subgroups, or phenotypes, with different underlying pathophysiological mechanisms. Imaging, genetics, biochemical biomarkers, and other features can be used to characterize phenotypes, but variations in data types can make it difficult to harmonize definitions. While radiography is widely used in KOA imaging, it is limited in its ability to assess early disease (when interventions are most likely to succeed) and is insensitive to change. Ultrasound (US) is a widely accessible, time-efficient and cost-effective imaging modality that can provide detailed and reliable information about all joint tissues (e.g., cartilage, meniscus, synovium, bone), and could therefore inform phenotypes in KOA (e.g., by presence of synovitis, effusion, cartilage damage, calcium crystal deposition, and popliteal cysts). Use of US is currently limited by the lack of systematically performed studies in well-characterized non-clinical populations. To address this gap and further the use of this advantageous imaging modality for KOA, we will obtain standardized US and radiography in the population- based Johnston County Health Study (JoCoHS), the new enrollment phase of the 25+ year Johnston County OA Project which includes white, African American, and Hispanic men and women aged 35-70, to achieve three aims. In Aim 1, we will determine the population prevalence (n~3000) of knee US features including cartilage and meniscal damage, synovitis/effusion, calcium crystal deposition, popliteal cysts and osteophytes overall and in key subgroups by age, sex, race/ethnicity, and symptom status. Aim 2 will allow quantification of the associations between these US features and radiographic findings and symptom scores overall and in key subgroups (e.g., those with and without radiographic KOA, by sex, by race/ethnicity). For Aim 3, we will apply novel machine learning methodologies (e.g., Direction-projection-permutation [DiProPerm] hypothesis testing, Joint and Individual Variation [JIVE], and Distance-Weighted Discrimination [DWD]) to a) develop an overall US score for symptomatic KOA and b) identify the contribution of US variables to phenotypes relevant to KOA based on general health, physical activity, and functional assessments. This study is a crucial step to establish the foundation for US as an assessment tool for clinical use, research, and clinical trials in KOA, providing unique population-based cross-sectional data regarding the utility of US and forming the basis for future longitudinal work evaluating its value and performance characteristics related to incident and progressive KOA.